The presence of noisy samples in distantly supervised data adversely affects the performance of models. Researchers have used different techniques in their models to mitigate the effects of noisy samples to make them more robust. Multi-instance relation extraction is one of the popular methods for noise mitigation. [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, and [8]} used this multi-instance learning concept in their proposed relation extraction models. For each entity pair, they used all the sentences that contained these two entities to find the relation between them. Their goal was to reduce the effect of noisy samples using this multi-instance setting. They used different types of sentence selection mechanisms to give importance to the sentences that contained relation-specific keywords and ignored the noisy sentences. [9]} and [5]} used the multi-task learning approach for mitigating the influence of noisy samples. They used fine-grained entity typing as an extra task in their model.
